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Understanding AlphaFold Metrics in Structure Evaluation

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AlphaFold is a deep learning model developed by the DeepMind team at Google for predicting the three-dimensional structure of proteins. In case, you are new to AlphaFold,  here  is a quick introduction to the program. AlphaFold uses a neural network to predict the three-dimensional structure of a protein from its amino acid sequence. The neural network is trained on a large dataset of known protein structures and amino acid sequences, using a technique called supervised learning. The model uses a combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to learn the complex relationships between protein sequences and structures. The output of the AlphaFold model is a prediction of the three-dimensional structure of the protein, represented as a set of coordinates for each atom in the protein. These predictions are evaluated using a range of metrics, including the predicted local distance difference test (PLDDT), predicted torsion angle metric (PTM), and i
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Tutorial_Pymol_3: Structure Editing in Pymol

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PyMOL is a powerful molecular visualization software that also allows users to edit molecular structures. Here are some ways to edit structures with PyMOL: If you are new to Pymol, I would recommend practicing  here . I can also recommend you slightly expert-level tutorial  here . 1. Add Atoms and Residues: To add atoms or residues to a structure, use the "Builder" function. First, select the residue or atom you want to add to. Then, go to "Wizard" > "Builder" and choose the atom or residue to add. 2. Delete Atoms and Residues: To delete atoms or residues from a structure, use the "Delete" function. First, select the atoms or residues you want to delete. Then, go to "Actions" > "Delete" and confirm the deletion. 3. Mutate Residues: To mutate a residue to another amino acid, use the "Mutagenesis" function. First, select the residue you want to mutate. Then, go to "Wizard" > "Mutagenesis"
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Pymol_Tutorial_2: Specialized Tricks of Pymol for Visualization

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Pymol is a powerful and versatile molecular visualization software that allows users to create high-quality 3D representations of molecular structures. Here are some specialized tricky and magical codes of Pymol. If you are new to Pymol, it is highly advised to practice  Basic Pymol Tutorial . In case you are looking to learn structure editing in Pymol, please follow  here . 1. Tricks Displaying secondary structure The following code displays the secondary structure of a protein using a color code where alpha helices are red and beta sheets are yellow. show cartoon set cartoon_ring_mode, 3 set cartoon_ladder_mode, 1 set cartoon_fancy_helices, 1 color red, ss h color yellow, ss s 2. Aligning structures The following code aligns two structures based on their alpha carbons and displays the aligned structures in different colors. align 1abc and chain A, 2xyz and chain B color blue, 1abc color green, 2xyz 3. Creating a surface representation The following code creates a surface representati
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Pymol_Tutorial_1. Complete Codes Used by PyMol in Generating High Quality Figures

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PyMol is an important package for analyzing and generating publication-quality or high-quality images. After reading this tutorial, you will be able to generate simple ribbon diagrams with labels if required. Generating specific surface representation to your system and the last section is all about how to generate high-quality figures of protein and ligands. The current document is a basic tutorial and those who are already familiar with Pymol are advised to follow  here.   Before going to the following sections, you are required to load your PDB file, and change your display color to the one intended. Display --- Background How to read the below part, if you are new? A sentence that starts with "#" is just an explanation of the code present in the very next line. Pymol offers you two parts in the GUI interface, and you can use the below codes in any interface. 1. Tutorial on generating a ribbon diagram and adding labels: # Load a protein structure load protein_name.pdb # Sh
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Tools for Generating Dataset of Homologue Sequences

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There are several bioinformatics tools that can be used to prepare a dataset of homologous protein sequences. Here are some commonly used tools:  Basic steps while using the below tools. BLAST: Basic Local Alignment Search Tool (BLAST) is a widely used sequence similarity search tool that can be used to find homologous sequences in a database. It can be run locally or online through the NCBI BLAST website. HMMER: HMMER is a software suite for protein sequence analysis that can be used to search sequence databases for homologous proteins using hidden Markov models (HMMs). It can be used to detect remote homologues that may not be detected by BLAST. CD-HIT: CD-HIT is a clustering tool that can be used to reduce redundancy in a set of homologous sequences. It clusters similar sequences together and removes redundant sequences based on a user-defined sequence identity threshold. MUSCLE: MUSCLE is a multiple-sequence alignment tool that can be used to align a set of homologous sequences.
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How to Generate a Dataset of Homologous Sequences?

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Preparing a list of homologues involves identifying and collecting protein sequences that are evolutionarily related and share a common ancestor. Here are some general steps that can be followed to prepare a list of homologues: Determine the protein of interest: Identify the protein of interest for which you want to find homologues. This protein should be well-characterized and have a known function.  Tools to Generate Homologues Dataset Perform a sequence search: Use a sequence search tool, such as BLAST or PSI-BLAST, to search for sequences that are similar to the protein of interest. These tools compare the protein sequence against a database of known protein sequences and return a list of hits that have significant sequence similarity. Filter the hits: The sequence search may return a large number of hits, many of which may be irrelevant or redundant. To filter the hits, you can set a threshold for sequence similarities, such as a minimum percentage identity or a minimum e-value.
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Selection Decision of the Scoring Functions in Molecular Docking

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When you have to perform molecular docking, the selection of an appropriate scoring function becomes necessary and that depends on the specific research question and the availability of computational resources. Both empirical and physics-based scoring functions have their own strengths and weaknesses, and the choice of the scoring function will depend on the accuracy and speed required for the particular research question.  What are scoring functions? Empirical scoring functions are faster and less computationally expensive than physics-based scoring functions. They are based on a statistical analysis of known protein-ligand complexes and use a set of parameters to describe the interactions between the protein and the ligand. Empirical scoring functions have been shown to accurately predict the binding affinities of a wide range of ligands to diverse proteins. Physics-based scoring functions, on the other hand, are based on physical principles such as force fields and quantum mechanics
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